Moreover, in the medical image domain, there is generally high interobserver variability in annotations, which can be considered label noise. For example, if an institution’s label quality is low, the model coming from that institution will adversely affect the global model; therefore, weights coming from that institution should be handled carefully. There are defense mechanisms such as KRUM [1]}, BARFED [2]}, or trimmed mean [3]} that can overcome the attacks in federated learning to some extent. These defense strategies may be used to overcome the label noise.
